/* 
Copyright (c) 2018. All rights reserved.
 
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
 
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/

#include "region.h"

const int          Region::numBoxesPerGrid = 5;
const float        Region::biases[Region::numBoxesPerGrid*2] = 
                        {
                            1.08,  1.19,  
                            3.42,  4.41,  
                            6.63,  11.38,  
                            9.42,  5.11,  
                            16.62, 10.52
                        };
const std::string Region::objectnames[] = 
                        { 
                            "aeroplane",
                            "bicycle",
                            "bird",
                            "boat",
                            "bottle",
                            "bus",
                            "car",
                            "cat",
                            "chair",
                            "cow",
                            "diningtable",
                            "dog",
                            "horse",
                            "motorbike",
                            "person",
                            "pottedplant",
                            "sheep",
                            "sofa",
                            "train",
                            "tvmonitor"
                        };

int Region::indexsort_comparator(const void *pa, const void *pb)
{
    float proba = ((indexsort *)pa)->prob[((indexsort *)pa)->index * ((indexsort *)pa)->channel + ((indexsort *)pa)->iclass];
    float probb = ((indexsort *)pb)->prob[((indexsort *)pb)->index * ((indexsort *)pb)->channel + ((indexsort *)pb)->iclass];

    float diff = proba - probb;
    if(diff < 0) return 1;
    else if(diff > 0) return -1;
    return 0;
}

float Region::logistic_activate(float x)
{
    return 1./(1. + exp(-x));
}

void Region::transpose(float *src, float* tar, int k, int n)
{
    int i, j, p;
    float *tmp = tar;
    for(i = 0; i < n; ++i)
    {
        for(j = 0, p = i; j < k; ++j, p += n)
        {
            *(tmp++) = src[p];
        }
    }
}

void Region::softmax(float *input, int n, float temp, float *output)
{
    int i;
    float sum = 0;
    float largest = input[0];
    for(i = 0; i < n; ++i){
        if(input[i] > largest) largest = input[i];
    }
    for(i = 0; i < n; ++i){
        float e = exp(input[i]/temp - largest/temp);
        sum += e;
        output[i] = e;
    }
    for(i = 0; i < n; ++i){
        output[i] /= sum;
    }
}

float Region::overlap(float x1, float w1, float x2, float w2)
{
    float l1 = x1 - w1/2;
    float l2 = x2 - w2/2;
    float left = l1 > l2 ? l1 : l2;
    float r1 = x1 + w1/2;
    float r2 = x2 + w2/2;
    float right = r1 < r2 ? r1 : r2;
    return right - left;
}

float Region::box_intersection(ibox a, ibox b)
{
    float w = overlap(a.x, a.w, b.x, b.w);
    float h = overlap(a.y, a.h, b.y, b.h);
    if(w < 0 || h < 0) return 0;
    float area = w*h;
    return area;
}

float Region::box_union(ibox a, ibox b)
{
    float i = box_intersection(a, b);
    float u = a.w*a.h + b.w*b.h - i;
    return u;
}

float Region::box_iou(ibox a, ibox b)
{
    return box_intersection(a, b)/box_union(a, b);
}

int Region::max_index(float *a, int n)
{
    if(n <= 0) return -1;
    int i, max_i = 0;
    float max = a[0];
    for(i = 1; i < n; ++i){
        if(a[i] > max){
            max = a[i];
            max_i = i;
        }
    }
    return max_i;
}

Region::Region(int c, int h, int w, int classes)
{
	size = 4 + classes + 1;
	totalLength = c * h * w;
	totalObjects = numBoxesPerGrid * h * w;
	output.resize(totalLength);
	boxes.resize(totalObjects);
	s.resize(totalObjects);

	for(int i = 0; i < totalObjects; ++i)
	{
		s[i].index = i;
		s[i].channel = size;
		s[i].prob = &output[5];
	}
}


PyObject*
Region::GetDetections(float* data, int c, int h, int w, 
                      int classes, int imgw, int imgh,
                      float thresh, float nms,
                      int blockwd)
{

	PyObject* objects = NULL ; 

	int i,j,k;

	transpose(data, &output[0], size*numBoxesPerGrid, w*h);

	// Initialize box, scale and probability
	for(i = 0; i < h*w*numBoxesPerGrid; ++i)
	{
		int index = i * size;
		//Box
		int n = i % numBoxesPerGrid;
		int row = (i/numBoxesPerGrid) / w;
		int col = (i/numBoxesPerGrid) % w;

		boxes[i].x = (col + logistic_activate(output[index + 0])) / blockwd; //w;
		boxes[i].y = (row + logistic_activate(output[index + 1])) / blockwd; //h;
		boxes[i].w = exp(output[index + 2]) * biases[2*n]   / blockwd; //w;
		boxes[i].h = exp(output[index + 3]) * biases[2*n+1] / blockwd; //h;

		//Scale
		output[index + 4] = logistic_activate(output[index + 4]);

		//Class Probability
		softmax(&output[index + 5], classes, 1, &output[index + 5]);
		for(j = 0; j < classes; ++j)
		{
			output[index+5+j] *= output[index+4];
			if(output[index+5+j] < thresh) output[index+5+j] = 0;
		}
	}

	//nms
	for(k = 0; k < classes; ++k)
	{
		for(i = 0; i < totalObjects; ++i)
		{
			s[i].iclass = k;
		}
		qsort(&s[0], totalObjects, sizeof(indexsort), this->indexsort_comparator);
		for(i = 0; i < totalObjects; ++i){
			if(output[s[i].index * size + k + 5] == 0) continue;
			ibox a = boxes[s[i].index];
			for(j = i+1; j < totalObjects; ++j){
				ibox b = boxes[s[j].index];
				if (box_iou(a, b) > nms){
					output[s[j].index * size + 5 + k] = 0;
				}
			}
		}
	}

	// generate objects
	for(i = 0, j = 5; i < totalObjects; ++i, j += size)
	{
		int iclass = max_index(&output[j], classes);

		double prob = output[j+iclass];

		if(prob > thresh)
		{
			ibox b = boxes[i];

			//printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);

			int left  = (b.x-b.w/2.)*imgw;
			int right = (b.x+b.w/2.)*imgw;
			int top   = (b.y-b.h/2.)*imgh;
			int bot   = (b.y+b.h/2.)*imgh;

			if(left < 0) left = 0;
			if(right > imgw-1) right = imgw-1;
			if(top < 0) top = 0;
			if(bot > imgh-1) bot = imgh-1;


            PyObject* objectBox = PyList_New(0);
            if ( objectBox ) {
                PyList_Append(objectBox, PyInt_FromLong(left));
                PyList_Append(objectBox, PyInt_FromLong(top));
                PyList_Append(objectBox, PyInt_FromLong(right));
                PyList_Append(objectBox, PyInt_FromLong(bot));
                PyList_Append(objectBox, PyFloat_FromDouble(prob));
                PyList_Append(objectBox, PyInt_FromLong(iclass));
                PyList_Append(objectBox, PyString_FromString(objectnames[iclass].c_str()));

                if ( !objects )
                {
                    objects = PyList_New(0);
                    Py_INCREF(objects);
                }
                Py_INCREF(objectBox);
                PyList_Append(objects, objectBox);
            } else {
                printf("Could not allocate memory for the object box.\n");
            }

		}
	}

    if ( ! objects )
        Py_RETURN_NONE ;

	return objects ;
}


#ifdef _REGION_MAIN
#include <iostream>
//g++ -g -D_REGION_MAIN -I./models/openVXModel -L/usr/lib region.cpp -lpython2.7

void initPython()
{
    Py_Initialize();
    PyObject *sysmodule = PyImport_ImportModule("sys");
    PyObject *syspath = PyObject_GetAttrString(sysmodule, "path");
    PyList_Append(syspath, PyString_FromString("."));
    Py_DECREF(syspath);
    Py_DECREF(sysmodule);
}

int main(int argc, char** argv)
{
    initPython();

    float inference_data[] = { \
2.53888160e-01,  2.23964322e-02, -1.45487890e-01, -1.07439002e-02, 5.91072366e-02,  1.36629790e-01,  1.32669359e-01,  1.24083139e-01, 1.29389778e-01,  5.11363707e-02,  5.65120056e-02,  1.28533021e-01, -3.88500184e-01,  4.82933760e-01, -2.24916905e-01,  7.13067576e-02, 1.84684992e-01, -7.37551227e-02, -2.27363408e-02, -7.20579252e-02, -3.84507421e-03, -2.58367639e-02,  2.02679485e-01,  4.47298169e-01, 1.40838847e-01,  8.80900323e-01, -6.09411657e-01, -2.27195442e-01, 1.71064064e-01, -3.65968257e-01, -3.86703163e-01, -2.50799984e-01, -1.24011606e-01, -7.40215331e-02,  2.43436992e-02,  5.37865698e-01, 7.50592649e-01,  1.44670236e+00, -9.90120053e-01, -4.91642952e-01, 2.37555444e-01, -5.42381406e-01, -4.01103735e-01, -4.63378429e-03, -7.28223249e-02, -2.39834294e-01, -2.89349765e-01,  3.56925726e-01, 4.78744984e-01,  7.47008204e-01, -1.51089203e+00, -4.62550044e-01, 5.24133325e-01,  4.24158007e-01,  4.51674581e-01,  2.82985270e-01, 1.32581517e-01, -5.64519987e-02, -8.09135437e-02,  4.41305876e-01, -2.81920675e-02, -3.67601693e-01, -9.99608636e-01,  3.33194137e-01, 4.71621245e-01,  9.24221218e-01,  7.11074889e-01,  2.52683491e-01, 2.41217554e-01, -5.64631075e-02, -1.13067731e-01,  4.04897124e-01, -1.83612183e-01, -7.83278584e-01, -1.03094019e-01,  5.36055207e-01, -2.43375152e-01,  1.12009913e-01, -2.07755953e-01, -2.29824428e-02, 1.76985726e-01, -5.15730269e-02, -8.74243900e-02,  1.78943798e-01, -2.51297265e-01, -2.52766032e-02,  2.36526966e-01,  3.22968185e-01, -5.81987917e-01,  1.11804083e-01, -5.14732301e-01, -6.49828374e-01, -1.09808162e-01,  1.12386346e-02, -2.74044842e-01, -1.18191354e-01, -6.29471958e-01,  4.36302334e-01,  3.88956189e-01,  1.42016158e-01, 1.30587267e-02,  6.07409298e-01, -1.15549900e-01, -3.20812881e-01, 1.45964995e-01,  1.04434453e-01, -3.65429640e-01, -5.19094527e-01, -5.91902792e-01,  2.46413678e-01, -2.41622314e-01, -4.61034507e-01, -1.46658361e-01,  5.81711084e-02, -1.18776895e-01, -2.23632842e-01, 7.73087502e-01,  1.17477155e+00,  5.02298117e-01, -3.00354123e-01, -4.21420455e-01,  6.37067631e-02, -2.19044648e-03, -2.12943017e-01, -1.56822890e-01, -2.51782089e-01, -2.29236111e-01, -3.09334576e-01, 1.17514394e-01,  3.42995644e-01,  7.35318303e-01,  3.75904024e-01, -2.11656898e-01, -2.29877373e-03,  2.93341074e-02, -3.28660570e-02, -3.33413519e-02, -1.55749112e-01, -1.16217680e-01, -8.99754018e-02, -3.16120125e-02, -1.52761430e-01,  1.28116861e-01,  1.06261000e-01, -5.78763127e-01, -6.60184979e-01, -4.88767028e-01, -6.31385684e-01, -7.51827419e-01, -7.77037144e-01, -8.18632066e-01, -8.24570537e-01, -8.64253342e-01, -8.05863261e-01, -8.46146286e-01, -5.29394805e-01, -8.11116248e-02,  3.69732618e-01,  3.67836446e-01, -1.67427376e-01, -4.64434296e-01, -3.61665219e-01, -3.96324813e-01, -4.45676744e-01, -5.89804828e-01, -5.33903480e-01, -4.86313671e-01, -2.10960224e-01, 4.45356041e-01,  1.62798858e+00,  9.85820651e-01,  6.15251064e-02, -2.43132412e-01, -2.22913474e-01, -2.70748615e-01, -1.06862001e-01, -4.25786823e-02, -1.24579035e-02, -2.01413874e-02, -1.49001136e-01, 8.13931882e-01,  1.33600163e+00,  1.08782959e+00,  7.32498109e-01, 4.31023419e-01,  3.16435754e-01,  6.10935271e-01,  5.59503555e-01, 3.92249376e-01,  2.48871908e-01,  2.41376851e-02, -7.71016553e-02, 4.31929350e-01, -5.45092642e-01, -4.47828859e-01, -4.26287949e-02, -9.38744657e-03,  7.54672825e-01,  1.83628500e+00,  1.52313089e+00, 1.13477206e+00,  7.46395588e-01,  3.52612406e-01,  1.26222475e-02, 1.04230356e+00, -7.49980435e-02, -1.93891581e-02,  1.03118038e+00, 1.19712591e+00,  1.11657548e+00,  8.75814080e-01,  4.01899070e-01, 3.55696291e-01,  4.71366376e-01,  6.02803946e-01,  4.44344401e-01, 5.66621363e-01,  9.86316025e-01,  2.18834114e+00,  2.77472711e+00, 2.48809004e+00,  1.87001944e+00,  1.43161345e+00,  1.17327380e+00, 1.22311544e+00,  1.13093221e+00,  1.32566643e+00,  8.23831618e-01, -1.33067882e+00, -9.77044225e-01,  1.47609860e-01,  1.90525860e-01, 1.84171602e-01,  1.00343955e+00,  1.64467967e+00,  1.88502669e+00, 2.00258112e+00,  2.20890260e+00,  2.15984559e+00,  1.07438016e+00, -1.36406994e+00, -1.52609527e+00, -1.90152061e+00, -2.89403081e+00, -3.02160525e+00, -2.65137100e+00, -2.50192428e+00, -1.91320693e+00, -1.39369965e+00, -2.88646966e-01,  5.73597491e-01,  5.98598957e-01, -2.44730532e-01, -4.08584118e-01, -7.36485004e-01, -1.14437211e+00, -1.27199221e+00, -1.89066052e+00, -2.67256069e+00, -2.36652255e+00, -2.46027446e+00, -2.66642737e+00, -2.00677943e+00, -4.25825953e-01, 8.34624171e-02,  1.02340274e-01, -1.22176223e-01, -8.39634566e-04, 1.05176605e-01,  6.79175332e-02, -1.07560597e-01, -3.24615479e-01, -8.28282595e-01, -1.37911081e+00, -1.38487244e+00, -1.69935787e+00, -1.71405688e-01,  2.94201046e-01,  2.79782057e-01,  2.92284936e-01, 3.93078327e-01,  3.29219043e-01,  2.95169771e-01,  3.57893258e-01, 4.68747050e-01,  4.70558643e-01,  3.19647133e-01, -6.20212257e-01, -2.89514095e-01,  1.42143760e-02,  2.54784822e-01,  4.22667384e-01, 3.97587806e-01,  3.41572464e-01,  2.98951268e-01,  2.97599822e-01, 2.99546778e-01,  2.68890709e-01,  3.13314497e-01, -2.95916438e-01, -8.66849244e-01, -5.66582739e-01, -3.46198946e-01, -6.31192401e-02, 1.52266219e-01,  1.74740270e-01,  1.13051973e-01,  1.15891241e-01, 1.36823609e-01,  9.54201296e-02,  2.44986609e-01, -6.16395831e-01, -1.11036694e+00, -8.17232668e-01, -6.10130548e-01, -3.66316080e-01, -5.17238379e-02, -1.95096672e-01, -2.94643193e-01, -1.39739186e-01, -4.74491492e-02, -1.52097106e-01, -4.85407934e-02, -9.53664362e-01, -1.09525299e+00, -8.72302353e-01, -8.09233725e-01, -5.03191769e-01, -4.05678511e-01, -7.22185314e-01, -1.02056336e+00, -7.81491518e-01, -4.82700944e-01, -6.30525291e-01, -3.21343660e-01, -1.13645172e+00, -6.56046391e-01, -7.26688683e-01, -7.87552834e-01, -3.58093888e-01, -2.68294662e-01, -7.49992311e-01, -1.04204261e+00, -5.82896292e-01, -2.89895147e-01, -5.10304153e-01, -2.97850668e-01, -1.13706553e+00, -2.70444095e-01, -3.17429274e-01, -4.37239736e-01, -3.68003696e-01, -3.20879102e-01, -6.19776607e-01, 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-6.74835026e-01,  2.67592967e-01, 1.47524029e-01,  2.87433058e-01,  6.34030700e-01,  3.67564648e-01, 6.55224442e-01,  1.94263995e+00,  1.93182409e+00,  1.55016458e+00, 3.57207879e-02,  2.60166854e-01,  2.52010882e-01,  1.21617949e+00, 8.58557820e-01,  1.23937798e+00,  1.26304281e+00,  8.10706079e-01, 1.34557664e+00,  1.94594717e+00,  1.20175946e+00,  1.10937715e+00, 5.58966577e-01,  8.99117768e-01,  1.40376580e+00,  2.05913854e+00, 1.29330051e+00,  1.39957213e+00,  1.07672310e+00,  6.36809230e-01, 1.36940789e+00,  1.92992759e+00,  6.38724208e-01,  7.90026546e-01, 1.07768357e+00,  1.22348905e+00,  1.30007386e+00,  1.55245447e+00, 1.02018344e+00,  9.50145841e-01,  7.11681545e-01,  6.01665258e-01, 1.59866059e+00,  2.35559821e+00,  1.14541006e+00,  5.71380436e-01 };
    int c=125 ;
    int h=12 ;
    int w=12 ;
    int classes=20 ;
    int imgw=416 ;
    int imgh=416 ;
    float thresh=0.18 ;
    float nms=0.4 ;
    int blocwd=13 ;

    Region test_region(c, h, w, classes);
    PyObject* objects = test_region.GetDetections(
                            inference_data, c, h, w, classes, 
                            imgw, imgh, thresh, nms, blocwd);

    Py_ssize_t n = PyList_Size(objects);
    for (int i=0; i<n; i++) {
        PyObject* a_list = PyList_GetItem(objects, i);
        Py_ssize_t m = PyList_Size(a_list);
        for (int j=0; j<m; j++) {
            PyObject* pItem = PyList_GetItem(a_list, j);
            if(PyInt_Check(pItem)) {
                printf("%ld ", PyInt_AsLong(pItem));
            } else if ( PyFloat_Check(pItem) ) {
                printf("%f ", PyFloat_AsDouble(pItem));
            } else if ( PyString_Check(pItem) ) {
                printf("%s ", PyString_AsString(pItem));
            } else {
                printf("unknown ");
            }
        }
        printf("\n");
    }

    Py_Finalize();
    return 0;
}
#endif
